• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.835-844
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• 2010

Tools for statistical analysis of extreme values include the classical annual maximum method, the modern threshold method and variants improving the second one. While the annual maximum method is to t th generalized extreme value distribution to the annual maxima of a time series, the threshold method is to the generalized Pareto distribution to the excesses over a high threshold from the series. In this paper we deal with the Poisson-GPD method, a variant of the threshold method with a further assumption that the total number of exceedances follows the Poisson distribution, and apply it to the daily percentage increases and decreases computed from the spot prices of West Texas Intermediate, which were collected from January 4th, 1988 until December 31st, 2009. According to this analysis, the distribution of daily percentage increases as well as decreases turns out to have a heavy tail, unlike the normal distribution, which coincides well with the general phenomenon appearing in the analysis of lots of nowaday nancial data.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.443-451
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• 2018

$P^M_{\lambda}$-service policy is a workload dependent hysteretic policy. The policy has two service states comprised of the ordinary stage and the fast stage. An ordinary service stage is initiated by the arrival of a customer in an idle state. When the workload of the server surpasses threshold ${\lambda}$, the ordinary service stage changes to the fast service state, and it continues until the system is empty. These service stages alternate in this manner. When the cost of changing service stages is high, the hysteretic policy is more efficient than the threshold policy, where a service stage changes immediately into the other service stage at either case of the workload's surpassing or crossing down a threshold. $P^M_{\lambda}$-service policy is a modification of $P^M_{\lambda}$-policy proposed to control finite dams, and also an extension of the well-known D-policy. The distributions of the stationary workload of $P^M_{\lambda}$-service policy and its variants are studied well. However, there is no known result on the sojourn time distribution. We prove that there is a relation between the sojourn time of a customer and the first up-crossing time of the workload process over the threshold ${\lambda}$ after the arrival of the customer. Using the relation and the duality of M/G/1 and G/M/1 queues, we obtain conditional sojourn time distributions in M/G/1 and G/M/1 queues under the policy.

• Journal of Korean Physical Therapy Science
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• v.2 no.2
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• pp.465-479
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• 1995

We randomly assigned 61 healthy subjects(male 14, female 47) to compare the experimental pain threshold and skin temperature between high mode TENS and burst mode TENS. In this study, 61 subjects were divided into three groups ; high mode TENS(n=20), burst mode TENS (n=20), and control group(n=21). Experimental pain thresholds and skin temperatures were measured before, immediately after cessation of stimulation, and at 30 minutes post stimulation. Stimulation was applied to the dorsal surface of the forearm(L14, LI10). Pain thresholds were measured by chronaxie meter. Skin temperature were measured by electrical digital thermometer. The results are as follows ; 1. There were no statistical difference in the pain threshold and skin temperature at before TENS stimulation among the three groups(p>0.05). 2. The pain threshold and skin temperature in burst mode TENS group was significantly higher and longer effect than that in high mode TENS group and control group(p<0.01). 3. The pain threshold in burst mode TENS group decreased to prestimulation levels by 30 minutes poststimulation. 4. The skin temperature in burst mode TENS group decreased to prestimulation levels by 20 minutes poststimulation. 5. The skin temperature was significantly difference among three group at immediately after, and at 30 minutes poststimulation and the skin temperature in burst mode TENS group was significantely higher than that in two groups(p<0.001). 6. The increasing rate of pain threshold in high mode TENS group after immediately cassation of stimulation was 24.3%(p<0.001). 7. The increasing rate of pain threshold in burst mode TENS group after immediately cessation of stimulation was 93.5% (p<0.001).

• Journal of Oral Medicine and Pain
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• v.20 no.1
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• pp.97-103
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• 1995

The purpose of this study was to evaluate the effect of experimenter gender on pain report as well as the sex differences in pain threshold and pain tolerance. Cold pressor test and pressure pain threshold (PPT) test were performed on forty dental students by both of a male and a female experimenter separately with 1 day interval. The obtained results were as follows : There were no differences in pain threshold and pain tolerance between males and females when they were examined by the same gender experimenter in the cold pressor test, but when they were examined by the opposite gender experimenter the pain threshold of males was significantly higher than females. When the pain threshold was measured by the same gender experimenter, using a algometer, there was no differences in PPT between males and females. However, when the same measurements were done by the opposite gender experimenter, the PPT of males was significantly higher than females at anterior temporalis and inferior masseter. For cold pressor test, females tended to report lower levels of pain threshold and pain tolerance to a male experimenter than a female, but the differences were not significant. Although both pain threshold and pain tolerance were increased when males were examined by a female experimenter in the cold pressor test, the statistical significance was found only in pain tolerance. When subjects were examined by the opposite gender experimenter in the PPT text, females reported significantly higher levels of pain at inferior masseter and males reported significantly lower levels of pain at anterior temporalis and inferior masseter.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1049-1068
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• 2014

Autoregressive models are used to analyze an univariate time series data; however, these methods can be inappropriate when a structural break appears in a time series since they assume that a trend is consistent. Threshold autoregressive models (popular regime-switching models) have been proposed to address this problem. Recently, the models have been extended to two regime-switching models with delay parameter. We discuss two regime-switching threshold autoregressive models from a Bayesian point of view. For a Bayesian analysis, we consider a parametric threshold autoregressive model and a nonparametric threshold autoregressive model using Dirichlet process prior. The posterior distributions are derived and the posterior inferences is performed via Markov chain Monte Carlo method and based on two Bayesian threshold autoregressive models. We present a simulation study to compare the performance of the models. We also apply models to gross domestic product data of U.S.A and South Korea.

• Journal of Communications and Networks
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• v.12 no.1
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• pp.74-85
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• 2010

For the purpose of compromising hosts, attackers including infected hosts initially perform a portscan using IP addresses in order to find vulnerable hosts. Considerable research related to portscan detection has been done and many algorithms have been proposed and implemented in the network intrusion detection system (NIDS). In order to distinguish portscanners from remote hosts, most portscan detection algorithms use a fixed threshold that is manually managed by the network manager. Because the threshold is a constant, even though the network environment or the characteristics of traffic can change, many false positives and false negatives are generated by NIDS. This reduces the efficiency of NIDS and imposes a high processing burden on a network management system (NMS). In this paper, in order to address this problem, we propose an automatic portscan detection system using an fast increase slow decrease (FISD) scheme, that will automatically and adaptively set the threshold based on statistical data for traffic during prior time periods. In particular, we focus on reducing false positives rather than false negatives, while the threshold is adaptively set within a range between minimum and maximum values. We also propose a new portscan detection algorithm, rate of increase in the number of failed connection request (RINF), which is much more suitable for our system and shows better performance than other existing algorithms. In terms of the implementation, we compare our scheme with other two simple threshold estimation methods for an adaptive threshold setting scheme. Also, we compare our detection algorithm with other three existing approaches for portscan detection using a real traffic trace. In summary, we show that FISD results in less false positives than other schemes and RINF can fast and accurately detect portscanners. We also show that the proposed system, including our scheme and algorithm, provides good performance in terms of the rate of false positives.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.12
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• pp.1669-1674
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• 2005

Maximum likelihood methodology was applied to analyze the efficiency and statistical power of interval mapping by using a threshold model. The factors that affect QTL detection efficiency (e.g. QTL effect, heritability and incidence of categories) were simulated in our study. Daughter design with multiple families was applied, and the size of segregating population is 500. The results showed that the threshold model has a great advantage in parameters estimation and power of QTL mapping, and has nice efficiency and accuracy for discrete traits. In addition, the accuracy and power of QTL mapping depended on the effect of putative quantitative trait loci, the value of heritability and incidence directly. With the increase of QTL effect, heritability and incidence of categories, the accuracy and power of QTL mapping improved correspondingly.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.755-767
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• 1997

Analyzing autocorrelated data set is still an open problem. Developing on easy and efficient method for severe positive correlated data set, which is common in simulation output, is vital for the simulation society. Bootstrap is on easy and powerful tool for constructing non-parametric inferential procedures in modern statistical data analysis. Conventional bootstrap algorithm requires iid assumption in the original data set. Proper choice of resampling units for generating replicates has much to do with the structure of the original data set, iid data or autocorrelated. In this paper, a new bootstrap resampling scheme is proposed to analyze the autocorrelated data set : the Threshold Bootstrap. A thorough literature search of bootstrap method focusing on the case of autocorrelated data set is also provided. Theoretical foundations of Threshold Bootstrap is studied and compared with other leading bootstrap sampling techniques for autocorrelated data sets. The performance of TB is reported using M/M/1 queueing model, else the comparison of other resampling techniques of ARMA data set is also reported.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.657-660
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• 2001

In this paper, a robust watermarking technique is presented by using a block-based statistics in DCT domain. First, the proposed technique calculates JND threshold value using the global statistics in DCT domain. Then watermark insertion is carried out by inserting one watermark into coefficients which are above the threshold value J within a 2${\times}$2 block. Finally, watermark is estimated by averaging the extracted watermarks from the coefficients which are above the threshold in a window. In experiments it is shown that the proposed techniques can enhance perceptual invisibility and robustness against additive noise and JPEG compression attacks by using the characteristics of JND.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.173-182
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• 2005

A Bayesian threshold model is considered to analyze binary or ordered categorical traits. Gibbs sampler for making full Bayesian inferences about the category probability as well as the regression coefficients is described. The model can be regarded as an alternative to the ordered logit regression model. Numerical examples are shown to demonstrate the efficiency of the model.